Inertial Navigation System

Localization and navigation systems with a lower acoustic signature are a requisite for AUVs carrying out surveillance operations. This study employs a Kalman filter (KF) data fusion algorithm in which the acceleration measurements from the inertial navigation system and the vehicle velocities predicted by a motion response mathematical model were combined with intermittent Doppler velocity log (DVL) measurements. The optimized solution was defined as 10% of DVL activation with additional 2.5% at the beginning and the end of the field trials.

Against the range-dependent accuracy of the tracking radar measurements including range, elevation and bearing angles, a new hybrid adaptive Kalman filter is proposed to enhance the performance of the radar aided strapdown inertial navigation system (INS/Radar). This filter involves the concept of residual-based adaptive estimation and adaptive fading Kalman filter, and tunes dynamically the filter parameters, including the fading factors and the measurement and process noises scaling factors based on the ratio of the actual residual covariance to the theoretical one. In fact, due to the unknown and fast- varying statistical parameters of the radar measurement noises and their nonlinear characteristics, apply- ing a conventional Kalman filter to INS/Radar fusion yields a low-performance navigation and in-flight alignment. The Monte Carlo simulation results of the integrated navigation system on an interceptor missile trajectory indicate the new algorithm has an effective performance in the face of nonlinearities and uncertainties of the tracking radar measurements. These results allow knowing whether the fine in- flight alignment and high-performance navigation can be possible for the long-range air defense missile using the low-cost INS/Radar system without aiding global navigation satellite system signals.

It is known that the algorithm of inertial navigation system is based upon integration of acceleration values of the launch vehicle sent by integrating accelerometers and reconstruction based upon calculation of the apparent way of its full position and the velocity in the coordinate system used to solve the navigation task by taking into account accelerations caused by the gravitational influence of the Earth [3, 4, 8, 9]. Let‘s consider structures of mathematical methods and algorithms needed to reconstruct all the functions of INS.

reliable positioning results, scene matching area selection is usually used for selecting those areas which are suitable for scene matching according to variance of gray scale, edge density, edge continuity and etc..[2-4]; and then route planning is used to make sure the aircraft enter into the matching areas; scene matching begins to conduct when the aircraft enters into the matching areas; revise the positioning accumulation error of INS after matching. Scene matching navigation system adopted by most of autonomous navigation missiles are designed based on the method described in [5].

In this paper, the goal is to develop the algorithm for integrated navigation of INS and velocity in order to improve the navigation performance of underwater vehicle in the sea water where the GPS electric wave cannot reach. The underwater vehicle will have the INS, depth sensor, ES (Echo Sounder), and DVL. Here the only de- vice used to measure the velocity as external sensor is DVL. However, as the DVL’s measuring scope depends on the characteristics of sensors, the general navigation using INS-DVL has its limit. For overcoming the limita- tion, the proposal is the velocity filter which uses the information from RPM (Revolve Per Minutes) of the un- derwater vehicle. The velocity filter is basically composed of the DVL, RPM, and ES and is realized using the Kalman filter. The integrated navigation filter is composed of the indirect feedback extended Kalman filter [8]. At this time, the difference between velocities obtained from velocity filter and pure navigation method is used as the measured value. To verify the performance of the proposed algorithm, simulation with scenario trajectory was conducted. The final results were compared with the PINS and conventional integrated navigation method for performance analysis.

model of a physical/dynamic system from the observed data. Six key steps are involved in system identification [7]; (1) Developing an approximate analytical model of structure, (2) Establishingthe levels of structural dynamic response which are likely to occur using the analytical model as well as thecharacteristics of anticipated excitation sources, (3) Determining the instrumentation requirements needed to sense the motion with prescribed accuracy and spatial resolution, (4) Performing experiments and recording the data, (5) Applying system identification techniques to identify the dynamic characteristics such as system matrixes, modal parameters, as well asexcitation and input/output noise characteristics, and (6)Refiningthe analytical model based on the identified results. The traditional identification techniques extracting modal parameters from input and output data have been well-developed and widely used in engineering. However, it is often a demandingtask to carry out excitation in the field testing of large engineering structures. To obviate such difficulties of the traditional techniques, methods of extracting modal parameters from structural response data have only been deeply investigated during the lastfew decades [8].Approximate dynamic programming (ADP) is a very effective method for the solution of Discrete- Time Nonlinear HJB [9]. There are many techniques of ADP to solve the cost function and hence the optimal control policy [10]. In [11], ADP techniques are classified into: heuristic dynamic programming (HDP), dual heuristic dynamic programming (DHP), action dependent heuristic dynamic programming (ADHDP), and action dependent dual heuristic dynamic programming (ADDHP).In [12], Liu and Li used optimal control for discrete-time HJB.

The following section describes a navigation algorithm which estimate the above mentioned parameters. The pose and ego-motion of the camera are derived using a DTM and the optical-flow field of two consecutive frames. Unlike the landmarks approach no specific fea- tures should be detected and matched. Only the corre- spondence between the two consecutive images should be found in order to derive the optical-flow field. As was mentioned in the previous section, a rough estimate of the required parameters is supplied as an input. Never- theless, since the algorithm only use this input as an ini- tial guess and re-calculate the pose and ego-motion di- rectly, no integration of previous errors will take place and accuracy will be preserved.

5 10 × − I × m s respectively. Also, the gyro and accelerometer scale factor are modeled as Random Constants and are equals to ( 0.01 ) I 3 3 × and ( 0.005 ) I 3 3 × respectively. Initial biases for the gyros and accelerometers are given by 10 deg/hr and 0.003 m/s 2 respectively, for each axis. The GPS data was generated by adding to the true positions and velocities of the reference trajectory a white Gaussian noise. The initial standard deviation of the horizontal position expressed in geographic coordinates and vertical posi- tion are equal to 8 × 10 –7 rd and 5 m respectively. The initial standard deviation of the horizontal and vertical velocities expressed in the navigation frame is equal to 15 m/s and 3 m/s respectively. The sampling frequency of the GPS is 10 Hz (100 ms). Simulated data of Earth’s magnetic field in the navigation frame were generated by the World Magnetic Model 2005 (WMM-2005) that uses the geographic position of the vehicle to determine its components. This reference magnetic field is transformed into the body frame and corrupted by a Gaussian white noise of zero-mean and standard deviation equal to 0.02 Gauss/s 1/2 . During the experiment, the raw data of the inertial navigation system (incremental angles and velocities) are provided at 200 Hz (5ms) whereas the magnetometer data (magnetic field) is supplied at 50 Hz (20 ms). To test the robustness of the Q-SUKF an ini- tial attitude error of 60 degrees is given in each axis. The diagonal terms of initial covariance matrix represent variances or mean squared errors. The off-diagonal terms are set to be zeros. The parameters used in the Q-SUKF developed are given by scaling parameters α = 0.05 and β = 2 , and by weight of 0th point

In strapdown inertial navigation system (SINS) [1] and orientation systems (SIOS) [9] of the aerospace aerial vehicle the classi- cal “Hamiltonian” quaternions of solid body rotation with the parameters of the Euler (Rod- rigues – Hamilton) [1], [9] are now (from the beginning of the 70s of the last century) widely used. These quaternions are normalized (with unit norm) and they cannot be zero [1–10].

Abstract. In order to solve the problem of geocoordinate measurement and trajectory distribution of buried long oil and gas pipelines, the positioning accuracy of pipeline is improved. In this paper, the data fusion of the mileage data by Kalman filtering algorithm is used to solve the segmented trajectories of gyroscope and accelerometer observation data collected by inertial navigation system. Calculating the total mileage of 49.902 km, each position error with checkpoint contrast are controlled to less than 1 m. The overall trend with map match exactly, integrated positioning accuracy is 0.928 ‰. It has a certain reference for using inertial navigation technology buried long distance oil and gas pipeline geographic coordinates measurement.

As shown in Fig. 2, the system can perform dead reckoning based on the speed measurement of odometer and the information on attitude provided by inertial navigation system. By building a Kalman filtering model of integrated navigation system based on inertial navigation and dead reckoning, forward Kalman filter is utilized for optimal filtering. Then backward filter is employed for smoothing, so as to obtain the movement track of inertial measurement unit inside pipeline. Eventually, the navigation error of system can be rectified by using the known high- precision position information at calibration ground points as well as the results of dead reckoning by odometer, so as to further improve the accuracy of positioning and complete the accurate measurement of pipeline track[9].

An inertial guidance system is one that is designed to fly a predetermined path. The missile is controlled by self- contained automatic devices called accelerometers.Accelerometers are inertial devices that measure accelerations. In missile control, they measure the vertical, lateral, and longitudinal accelerations of the controlled missile (Ref. fig. 6) .Although there may not be contact between the launching site and the missile after launch, the missile is able to make corrections to its flight path with amazing precision. During flight, unpredictable outside forces, such as wind, work on the missile, causing changes in speed commands. These commands are transmitted to the missile by varying the characteristics of the missile tracking or guidance beam, or by the use of a separate radio uplink transmitter. This data is taken by onboard computers and converted onto precise position of the missile.Lately, however, inertial systems have been combined with GPS (Global Positioning System) to navigate missiles more accurately. However, even with the best inertial systems available, missiles suffer from a phenomenon called drift. This is measured in distance (meters) per hour. For example, during the making of the Tomahawk Cruise missiles it was determined that even with the inertial navigation system, it would have a drift of 900 meters per hour. This essentially means that if the missile flew for one hour, it could miss the target by as much as 900 meters! Further, while ICBMs travel at sonic and supersonic speeds, smaller Cruise’s speed was subsonic. So, the chances of missing the target are higher.

Nowadays, inertial navigation systems are widely used to determine the location of vehicles such as: aircrafts, helicop- ters, cars [6], [7], [10] ... The core of an inertial navigation system is the inertial measurement unit (IMU) which is used to measure the linear acceleration and the angular velocity of rotation. By combining these signals in the real-time domain, an inertial navigation system is able to observe the position, velocity and direction of the vehicle. However, the estimating process can not be used for long periods because the uncer- tainty in the calculation process will increase over time. Causes of this error are noise and systematic errors in the inertial measurement. In this situation, current inertial navi- gation systems often combine GPS and the extended Kalman filter to get the smallest position error. However, during loss of GPS signals the system will encounter dangerous re- strictions: errors during navigation state estimation quickly accumulate over time due to a big nonlinear drift phenomena in measurements of inertial sensors.

Cycling is an increasingly popular mode of travel in cities owing to the great advantages that it offers in terms of space consumption, health and environmental sustainability. However, the number of recent accidents between cyclists and heavy goods vehicles has increased substantially. Our study shows that one of the main causes of accidents is drivers not being able to observe cyclists. Thus, this research reported here involves the development of an innovative low-cost technological solution called Cyclist 360° Alert and as an integral part of this system, this paper focuses on the bicycle localization aspect and presents an approach based on low-cost micro-electromechanical systems (MEMS) sensor configurations on an instrumented prototype bicycle system, called “iBike”. The iBike has the capability of sensing its motion, which can be then analysed to compute the trajectory path. The paper describes the overall system of the instrumented bicycle which incorporates an Inertial Navigation System (INS) and a Global Navigation Satellite System (GNSS) receiver. The paper then evaluates and compare the accuracy of the three positioning systems using experimental field data. Finally, the paper also draws conclusions on the applicability of specific sensor configurations, both in terms of sensors’ accuracy and reliability with respect to the measurements of motion, and the ability of tracking trajectories based on the data gathered from the sensors.

Our goal was to implement and construct a simple land vehicle Inertial Navigation System (LVNS) using micro- electromechanical systems (MEMS) sensor. An inertial navigation (INS) system was designed using state of the commercial inertial sensors on a low cost budget. The system uses a 3 axis accelerometer, 3 axis gyros to detect the orientation, velocity and position which is calculated and displayed by the system for the user to view. This paper was performed on the assumption that this system would be used underground, inside a building, whereas global positioning data (GPS) cannot be received and so efficient inertial movement tracking is the only plausible method for the system to track current velocity and position. Without using any external device or sensor as aided system, where used (INS) in conjunction with zero velocity update (ZUPT) method to fulfil the demands of system. This paper evaluates the positioning capability of INS/ZUPT integrated systems utilizing low cost (IMU) for INS

Alignment is the process whereby the orientation of the axes of an inertial navigation system is determined with respect to the reference axis system. The basic concept of aligning an inertial navigation system is quite simple and straightforward. However, there are many complications that make alignment both time consuming and complex. Consider a simulated transport ship-airplane system. Suppose that the base coordinate system (BCS) of the ship is correct. Let − → Ω 1 be the

It was shown that using the RIMU, the IMU error observability increased, particularly the z-axis gyro bias. This is a significant result towards having an autonomous inertial pedestrian navigation system because the remaining unobservable errors can now be made observable even without extra measurements to feed the KF. Nonetheless, more trials are worth performing to assess the RIMU’s true capabilities once a better RIMU prototype is available. This is because the current RIMU prototype is too impractical for mass trials due to its weight and size. Once all components can be miniaturized, it will be more practical to attach them to a foot or shoe. Trials such as a real fire-fighter trial are thought to be useful to assess the RIMU’s performance as it is supposed to better estimate (or observe) the error terms, regardless of the IMU bias variations that could be caused by, for example, extreme temperatures. Ultimately, this should further improve the navigation accuracy of an autonomous low-cost foot-mounted inertial navigation system.

In this paper, a mechanism is designed for integration of inertial navigation system information (INS) and global positioning system information (GPS). In this type of system, a series of mathematical and filtering algorithms with Tightly Coupled techniques with several objectives such as application of integrated navigation algorithms, precise calculation of flying object position, speed and attitudes at any moment. Obviously, GPS speed information will contribute to make better estimates of the state in addition to location information. Typically, Kalman filter provides optimal method for states estimation and also creates possibility of combining several measurements to acquiesce a united estimate of system status. Tightly Coupled Kalman filter is a novel and applicable approach to effectively track path with high accuracy especially when four satellites are not available or satellite system stops along the route. Indeed, an important advantage of integration with Tightly Coupled filtration is related to application of software system rather than hardware which somehow reduces hardware complexity and also other advantages of sensors integration is associated with application of all benefits of various sensors as well as covering their individual imperfections in order to increase navigation accuracy. Generally, in integration systems exact GPS observations are used to estimate and INS errors modification by Kalman filter. It is expected that an integrated system with high-precision provides an accurate estimation of all unknowns’ parameters and states through kalman filter. Simulations executed for integrated navigation system demonstrate that a flying object could sufficiently compensate errors resulting from modeling of inertial navigation that grows integrally over time and also impressively inhibit flying object deviation respect to condition that only GPS location information are available.

The BDS is created by China’s independent research and development, which is the world’s fourth largest navigation system, and other three are the United States Global Positioning System (GPS), the Russian “GLONASS” system, the European “Galileo” system, which are more mature [1–5]. Due to different working environments and carriers, a navigation system must be equipped with different combinations of different properties of a circularly-polarized antenna, to get better wireless communication performance and applications [6]. The BDS plays an important role in both military and civilian fields. The BeiDou antenna as an essential part of the satellite communications decides information receiving and sending [7]. Based on the first and second generations of the BDS, the Beidou antenna can be used to solve the problem of poor isolation of a multi-frequency antenna layer [8, 9], which is suitable for space, vehicle and ship navigation and other equipment [10–12].

Blue tooth module connected with a controller to transmit sensor data to a PC is placed on each robot. The entire system consists of a wireless network based on the blue tooth protocol. A coordinator node Bluetooth module connected to a personal computer is used to create and hold the network, acquire and process data. In this work, mobile nodes (hexapod robot) used for localisation, and coordinator node used to hold the network and collect data. Software is then developed and placed in the PC for managing the network and saving data in the data base.